Sensing and machine learning for automotive perception: A review
Automotive perception involves understanding the external driving environment and the
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …
internal state of the vehicle cabin and occupants using sensor data. It is critical to achieving …
Cdtrans: Cross-domain transformer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
source domain to a different unlabeled target domain. Most existing UDA methods focus on …
Parallel vision for intelligent transportation systems in metaverse: Challenges, solutions, and potential applications
Metaverse and intelligent transportation system (ITS) are disruptive technologies that have
the potential to transform the current transportation system by decreasing traffic accidents …
the potential to transform the current transportation system by decreasing traffic accidents …
Divide and contrast: Source-free domain adaptation via adaptive contrastive learning
We investigate a practical domain adaptation task, called source-free domain adaptation
(SFUDA), where the source pretrained model is adapted to the target domain without access …
(SFUDA), where the source pretrained model is adapted to the target domain without access …
Patch-mix transformer for unsupervised domain adaptation: A game perspective
Endeavors have been recently made to leverage the vision transformer (ViT) for the
challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross …
challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross …
Learning background prompts to discover implicit knowledge for open vocabulary object detection
Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable
of recognizing objects from both base and novel categories. Recent advances leverage …
of recognizing objects from both base and novel categories. Recent advances leverage …
Divide and adapt: Active domain adaptation via customized learning
Active domain adaptation (ADA) aims to improve the model adaptation performance by
incorporating the active learning (AL) techniques to label a maximally-informative subset of …
incorporating the active learning (AL) techniques to label a maximally-informative subset of …
[HTML][HTML] Semi-supervised bidirectional alignment for remote sensing cross-domain scene classification
Remote sensing (RS) image scene classification has obtained increasing attention for its
broad application prospects. Conventional fully-supervised approaches usually require a …
broad application prospects. Conventional fully-supervised approaches usually require a …
Semi-supervised domain adaptation with source label adaptation
Abstract Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen
target data with a few labeled and lots of unlabeled target data, along with many labeled …
target data with a few labeled and lots of unlabeled target data, along with many labeled …
Alignsam: Aligning segment anything model to open context via reinforcement learning
Powered by massive curated training data Segment Anything Model (SAM) has
demonstrated its impressive generalization capabilities in open-world scenarios with the …
demonstrated its impressive generalization capabilities in open-world scenarios with the …